451 citations · 1.7k across the 73 of their papers we have counts for
5 papers · 1 filter
Model-based Reinforcement Learning and the Eluder Dimension
Ian Osband, Benjamin Van Roy
We consider the problem of learning to optimize an unknown Markov decision process (MDP). We show that, if the MDP can be parameterized within some known function class, we can obt…
Learning to Optimize via Information-Directed Sampling
Daniel Russo, Benjamin Van Roy
We propose information-directed sampling -- a new approach to online optimization problems in which a decision-maker must balance between exploration and exploitation while learnin…
An Information-Theoretic Analysis of Thompson Sampling
Daniel Russo, Benjamin Van Roy
We provide an information-theoretic analysis of Thompson sampling that applies across a broad range of online optimization problems in which a decision-maker must learn from partia…
Near-optimal Reinforcement Learning in Factored MDPs
Ian Osband, Benjamin Van Roy
Any reinforcement learning algorithm that applies to all Markov decision processes (MDPs) will suffer regret on some MDP, where is the elapsed time and and…
Generalization and Exploration via Randomized Value Functions
Ian Osband, Benjamin Van Roy, Zheng Wen
We propose randomized least-squares value iteration (RLSVI) -- a new reinforcement learning algorithm designed to explore and generalize efficiently via linearly parameterized valu…